2008 EffectiveLabelAcquisitionforCol

(Bilgic et al., 2008) ⇒ Mustafa Bilgic, and Lise Getoor. (2008). “Effective Label Acquisition for Collective Classification.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401901

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Abstract

Information diffusion, viral marketing, and collective classification all attempt to model and exploit the relationships in a network to make inferences about the labels of nodes. A variety of techniques have been introduced and methods that combine attribute information and neighboring label information have been shown to be effective for collective labeling of the nodes in a network. However, in part because of the correlation between node labels that the techniques exploit, it is easy to find cases in which, once a misclassification is made, incorrect information propagates throughout the network. This problem can be mitigated if the system is allowed to judiciously acquire the labels for a small number of nodes. Unfortunately, under relatively general assumptions, determining the optimal set of labels to acquire is intractable. Here we propose an acquisition method that learns the cases when a given collective classification algorithm makes mistakes, and suggests acquisitions to correct those mistakes. We empirically show on both real and synthetic datasets that this method significantly outperforms a greedy approximate inference approach, a viral marketing approach, and approaches based on network structural measures such as node degree and network clustering. In addition to significantly improving accuracy with just a small amount of labeled data, our method is tractable on large networks.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 EffectiveLabelAcquisitionforCol	Mustafa Bilgic			Effective Label Acquisition for Collective Classification				10.1145/1401890.1401901